Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = '/data'
!pip install matplotlib==2.0.2
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Collecting matplotlib==2.0.2
  Downloading https://files.pythonhosted.org/packages/60/d4/6b6d8a7a6bc69a1602ab372f6fc6e88ef88a8a96398a1a25edbac636295b/matplotlib-2.0.2-cp36-cp36m-manylinux1_x86_64.whl (14.6MB)
    100% |████████████████████████████████| 14.6MB 45kB/s  eta 0:00:01   53% |█████████████████▏              | 7.8MB 44.6MB/s eta 0:00:01
Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: pyparsing!=2.0.0,!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib==2.0.2)
Requirement already satisfied: numpy>=1.7.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: six>=1.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: pytz in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Installing collected packages: matplotlib
  Found existing installation: matplotlib 2.1.0
    Uninstalling matplotlib-2.1.0:
      Successfully uninstalled matplotlib-2.1.0
Successfully installed matplotlib-2.0.2
You are using pip version 9.0.1, however version 18.0 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fa3e05085c0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fa3e03f32b0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_inputs = tf.placeholder(tf.float32,shape=(None, image_width, image_height, image_channels), name='input_real')
    z_input = tf.placeholder(tf.float32,shape=(None, z_dim), name='z_input')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return real_inputs, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
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==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    
    with tf.variable_scope('discriminator', reuse=reuse):
        conv1= tf.layers.conv2d(images, 64, kernel_size=4, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv1= tf.maximum(0.2*conv1, conv1)
        
        conv2= tf.layers.conv2d(conv1, 128, kernel_size=4, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv2= tf.layers.batch_normalization(conv2, training=True)
        conv2= tf.maximum(0.2*conv2, conv2)
        
        conv3= tf.layers.conv2d(conv2, 256, kernel_size=4, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv3= tf.layers.batch_normalization(conv3, training=True)
        conv3= tf.maximum(0.2*conv3, conv3)
        
        conv4= tf.layers.conv2d(conv3, 512, kernel_size=4, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv4= tf.layers.batch_normalization(conv4, training=True)
        conv4= tf.maximum(0.2*conv4, conv4)
        
        flat= tf.reshape(conv4, (-1, 4*4*512))
        logits= tf.layers.dense(flat,1, kernel_initializer=tf.truncated_normal_initializer(stddev=0.02))
        logits = tf.layers.dropout(logits, rate=0.5)
        output= tf.sigmoid(logits)
        
        

    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not is_train):
        layer1 = tf.layers.dense(z, 2*2*512, kernel_initializer=tf.truncated_normal_initializer(stddev=0.02))
        layer1 = tf.reshape(layer1, (-1, 2, 2, 256))
        layer1 = tf.nn.relu(layer1)
                
        layer2 = tf.layers.conv2d_transpose(layer1, 256, 5, strides=2, padding='valid', kernel_initializer=tf.contrib.layers.xavier_initializer())
        layer2 = tf.layers.batch_normalization(layer2, training=is_train)
        layer2 = tf.nn.relu(layer2)

        layer3 = tf.layers.conv2d_transpose(layer2, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        layer3 = tf.layers.batch_normalization(layer3, training=is_train)
        layer3 = tf.nn.relu(layer3)

        logits = tf.layers.conv2d_transpose(layer3, out_channel_dim, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())       
        output = tf.tanh(logits)
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    smooth = 0.1
    generator_model = generator(input_z, out_channel_dim)
    discriminator_real, discriminator_logits_real = discriminator(input_real, reuse=False)
    discriminator_fake, discriminator_logits_fake = discriminator(generator_model, reuse=True)
    
    discriminator_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=discriminator_logits_real, labels=tf.ones_like(discriminator_real*(1-smooth))))
    discriminator_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=discriminator_logits_fake, labels=tf.zeros_like(discriminator_fake)))
    generator_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=discriminator_logits_fake, labels=tf.ones_like(discriminator_fake)))
    
    discriminator_loss = discriminator_loss_real + discriminator_loss_fake
    return discriminator_loss, generator_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    train_vars = tf.trainable_variables()
    discr_vars = [var for var in train_vars if var.name.startswith('discriminator')]
    gener_vars = [var for var in train_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        disc_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=discr_vars)
        gene_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=gener_vars)
    
    return disc_train_opt, gene_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [15]:
batch_size = 64
z_dim = 100
learning_rate = 0.0004
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 3.1666... Generator Loss: 6.8487
Epoch 1/2... Batch 20... Discriminator Loss: 2.4362... Generator Loss: 5.7362
Epoch 1/2... Batch 30... Discriminator Loss: 0.6308... Generator Loss: 0.9871
Epoch 1/2... Batch 40... Discriminator Loss: 3.3800... Generator Loss: 0.0386
Epoch 1/2... Batch 50... Discriminator Loss: 0.3897... Generator Loss: 1.2514
Epoch 1/2... Batch 60... Discriminator Loss: 0.4587... Generator Loss: 1.1407
Epoch 1/2... Batch 70... Discriminator Loss: 0.1396... Generator Loss: 2.4082
Epoch 1/2... Batch 80... Discriminator Loss: 0.6558... Generator Loss: 1.1508
Epoch 1/2... Batch 90... Discriminator Loss: 3.2647... Generator Loss: 0.0667
Epoch 1/2... Batch 100... Discriminator Loss: 1.5143... Generator Loss: 0.4232
Epoch 1/2... Batch 110... Discriminator Loss: 1.7353... Generator Loss: 0.2346
Epoch 1/2... Batch 120... Discriminator Loss: 1.3330... Generator Loss: 0.3679
Epoch 1/2... Batch 130... Discriminator Loss: 1.5389... Generator Loss: 0.2857
Epoch 1/2... Batch 140... Discriminator Loss: 1.6158... Generator Loss: 0.2870
Epoch 1/2... Batch 150... Discriminator Loss: 1.5050... Generator Loss: 0.3364
Epoch 1/2... Batch 160... Discriminator Loss: 1.7399... Generator Loss: 0.2805
Epoch 1/2... Batch 170... Discriminator Loss: 1.5236... Generator Loss: 0.3003
Epoch 1/2... Batch 180... Discriminator Loss: 1.7385... Generator Loss: 0.2381
Epoch 1/2... Batch 190... Discriminator Loss: 1.8366... Generator Loss: 0.2144
Epoch 1/2... Batch 200... Discriminator Loss: 1.5461... Generator Loss: 0.2820
Epoch 1/2... Batch 210... Discriminator Loss: 1.5721... Generator Loss: 0.2764
Epoch 1/2... Batch 220... Discriminator Loss: 2.1513... Generator Loss: 0.1402
Epoch 1/2... Batch 230... Discriminator Loss: 1.9509... Generator Loss: 1.2954
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Epoch 1/2... Batch 420... Discriminator Loss: 1.8589... Generator Loss: 0.1971
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Epoch 1/2... Batch 490... Discriminator Loss: 0.9896... Generator Loss: 1.6755
Epoch 1/2... Batch 500... Discriminator Loss: 1.2722... Generator Loss: 1.3864
Epoch 1/2... Batch 510... Discriminator Loss: 0.8061... Generator Loss: 1.5582
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Epoch 1/2... Batch 530... Discriminator Loss: 1.0949... Generator Loss: 1.8550
Epoch 1/2... Batch 540... Discriminator Loss: 0.8331... Generator Loss: 1.5522
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Epoch 1/2... Batch 580... Discriminator Loss: 1.5546... Generator Loss: 1.6383
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Epoch 1/2... Batch 600... Discriminator Loss: 1.0610... Generator Loss: 0.6776
Epoch 1/2... Batch 610... Discriminator Loss: 1.1463... Generator Loss: 1.6081
Epoch 1/2... Batch 620... Discriminator Loss: 2.4621... Generator Loss: 2.2134
Epoch 1/2... Batch 630... Discriminator Loss: 1.0269... Generator Loss: 1.1492
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Epoch 1/2... Batch 650... Discriminator Loss: 1.6404... Generator Loss: 0.2473
Epoch 1/2... Batch 660... Discriminator Loss: 2.2726... Generator Loss: 0.1293
Epoch 1/2... Batch 670... Discriminator Loss: 1.3796... Generator Loss: 0.3709
Epoch 1/2... Batch 680... Discriminator Loss: 1.7269... Generator Loss: 0.2227
Epoch 1/2... Batch 690... Discriminator Loss: 1.9571... Generator Loss: 0.1890
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Epoch 1/2... Batch 770... Discriminator Loss: 1.0956... Generator Loss: 0.7438
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Epoch 2/2... Batch 230... Discriminator Loss: 1.0653... Generator Loss: 1.6976
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Epoch 2/2... Batch 260... Discriminator Loss: 0.6201... Generator Loss: 1.4156
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Epoch 2/2... Batch 280... Discriminator Loss: 1.7364... Generator Loss: 2.9749
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Epoch 2/2... Batch 300... Discriminator Loss: 0.6216... Generator Loss: 1.8074
Epoch 2/2... Batch 310... Discriminator Loss: 1.7756... Generator Loss: 3.5343
Epoch 2/2... Batch 320... Discriminator Loss: 0.6779... Generator Loss: 1.2557
Epoch 2/2... Batch 330... Discriminator Loss: 0.9705... Generator Loss: 0.6543
Epoch 2/2... Batch 340... Discriminator Loss: 2.1967... Generator Loss: 0.1481
Epoch 2/2... Batch 350... Discriminator Loss: 1.6803... Generator Loss: 0.2624
Epoch 2/2... Batch 360... Discriminator Loss: 2.1390... Generator Loss: 0.1536
Epoch 2/2... Batch 370... Discriminator Loss: 1.7280... Generator Loss: 0.2356
Epoch 2/2... Batch 380... Discriminator Loss: 1.8390... Generator Loss: 0.2405
Epoch 2/2... Batch 390... Discriminator Loss: 1.8956... Generator Loss: 0.2071
Epoch 2/2... Batch 400... Discriminator Loss: 1.2219... Generator Loss: 0.4845
Epoch 2/2... Batch 410... Discriminator Loss: 1.1325... Generator Loss: 1.6769
Epoch 2/2... Batch 420... Discriminator Loss: 1.2740... Generator Loss: 0.4368
Epoch 2/2... Batch 430... Discriminator Loss: 1.4646... Generator Loss: 2.1229
Epoch 2/2... Batch 440... Discriminator Loss: 0.5153... Generator Loss: 1.4393
Epoch 2/2... Batch 450... Discriminator Loss: 2.0362... Generator Loss: 0.2084
Epoch 2/2... Batch 460... Discriminator Loss: 1.6528... Generator Loss: 0.2474
Epoch 2/2... Batch 470... Discriminator Loss: 2.0271... Generator Loss: 0.1710
Epoch 2/2... Batch 480... Discriminator Loss: 1.9973... Generator Loss: 0.1732
Epoch 2/2... Batch 490... Discriminator Loss: 1.9671... Generator Loss: 0.1801
Epoch 2/2... Batch 500... Discriminator Loss: 1.3934... Generator Loss: 0.3671
Epoch 2/2... Batch 510... Discriminator Loss: 1.3120... Generator Loss: 0.4366
Epoch 2/2... Batch 520... Discriminator Loss: 2.8882... Generator Loss: 5.0015
Epoch 2/2... Batch 530... Discriminator Loss: 0.8607... Generator Loss: 1.7131
Epoch 2/2... Batch 540... Discriminator Loss: 1.3308... Generator Loss: 2.2312
Epoch 2/2... Batch 550... Discriminator Loss: 0.7927... Generator Loss: 1.0316
Epoch 2/2... Batch 560... Discriminator Loss: 1.0325... Generator Loss: 0.5529
Epoch 2/2... Batch 570... Discriminator Loss: 0.8591... Generator Loss: 0.8802
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Epoch 2/2... Batch 600... Discriminator Loss: 0.7209... Generator Loss: 2.4459
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Epoch 2/2... Batch 710... Discriminator Loss: 1.5113... Generator Loss: 3.1994
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Epoch 2/2... Batch 870... Discriminator Loss: 2.7242... Generator Loss: 0.0896
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Epoch 2/2... Batch 890... Discriminator Loss: 1.3441... Generator Loss: 2.0911
Epoch 2/2... Batch 900... Discriminator Loss: 1.8622... Generator Loss: 0.2103
Epoch 2/2... Batch 910... Discriminator Loss: 2.3584... Generator Loss: 0.1392
Epoch 2/2... Batch 920... Discriminator Loss: 0.4782... Generator Loss: 1.4241
Epoch 2/2... Batch 930... Discriminator Loss: 2.2647... Generator Loss: 0.1417
In [14]:
batch_size = 64
z_dim = 200
learning_rate = 0.0002
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 4.8471... Generator Loss: 0.0121
Epoch 1/1... Batch 20... Discriminator Loss: 1.2689... Generator Loss: 0.4679
Epoch 1/1... Batch 30... Discriminator Loss: 0.4461... Generator Loss: 1.4530
Epoch 1/1... Batch 40... Discriminator Loss: 2.4859... Generator Loss: 0.0970
Epoch 1/1... Batch 50... Discriminator Loss: 0.8678... Generator Loss: 0.6541
Epoch 1/1... Batch 60... Discriminator Loss: 0.8491... Generator Loss: 0.6705
Epoch 1/1... Batch 70... Discriminator Loss: 0.2003... Generator Loss: 5.0038
Epoch 1/1... Batch 80... Discriminator Loss: 1.8470... Generator Loss: 2.9902
Epoch 1/1... Batch 90... Discriminator Loss: 1.2208... Generator Loss: 0.7275
Epoch 1/1... Batch 100... Discriminator Loss: 0.8703... Generator Loss: 3.0516
Epoch 1/1... Batch 110... Discriminator Loss: 1.3640... Generator Loss: 0.5321
Epoch 1/1... Batch 120... Discriminator Loss: 0.9645... Generator Loss: 0.7097
Epoch 1/1... Batch 130... Discriminator Loss: 0.7826... Generator Loss: 0.9134
Epoch 1/1... Batch 140... Discriminator Loss: 1.6208... Generator Loss: 0.3762
Epoch 1/1... Batch 150... Discriminator Loss: 1.4588... Generator Loss: 0.3853
Epoch 1/1... Batch 160... Discriminator Loss: 1.3240... Generator Loss: 0.8558
Epoch 1/1... Batch 170... Discriminator Loss: 1.1666... Generator Loss: 1.6954
Epoch 1/1... Batch 180... Discriminator Loss: 1.2544... Generator Loss: 1.5902
Epoch 1/1... Batch 190... Discriminator Loss: 0.8642... Generator Loss: 1.3669
Epoch 1/1... Batch 200... Discriminator Loss: 0.9434... Generator Loss: 2.1580
Epoch 1/1... Batch 210... Discriminator Loss: 1.4770... Generator Loss: 0.3254
Epoch 1/1... Batch 220... Discriminator Loss: 1.0209... Generator Loss: 2.1620
Epoch 1/1... Batch 230... Discriminator Loss: 0.6148... Generator Loss: 1.6653
Epoch 1/1... Batch 240... Discriminator Loss: 0.8890... Generator Loss: 2.4527
Epoch 1/1... Batch 250... Discriminator Loss: 0.6653... Generator Loss: 1.4308
Epoch 1/1... Batch 260... Discriminator Loss: 0.8973... Generator Loss: 0.7944
Epoch 1/1... Batch 270... Discriminator Loss: 0.8524... Generator Loss: 1.0594
Epoch 1/1... Batch 280... Discriminator Loss: 1.1826... Generator Loss: 0.7074
Epoch 1/1... Batch 290... Discriminator Loss: 1.7102... Generator Loss: 0.2863
Epoch 1/1... Batch 300... Discriminator Loss: 1.3772... Generator Loss: 0.4763
Epoch 1/1... Batch 310... Discriminator Loss: 2.0803... Generator Loss: 0.1803
Epoch 1/1... Batch 320... Discriminator Loss: 1.3048... Generator Loss: 0.4054
Epoch 1/1... Batch 330... Discriminator Loss: 1.4669... Generator Loss: 0.3207
Epoch 1/1... Batch 340... Discriminator Loss: 1.6643... Generator Loss: 0.2500
Epoch 1/1... Batch 350... Discriminator Loss: 1.2633... Generator Loss: 0.4522
Epoch 1/1... Batch 360... Discriminator Loss: 2.0981... Generator Loss: 0.1743
Epoch 1/1... Batch 370... Discriminator Loss: 0.7605... Generator Loss: 0.9785
Epoch 1/1... Batch 380... Discriminator Loss: 0.9781... Generator Loss: 1.1819
Epoch 1/1... Batch 390... Discriminator Loss: 1.1353... Generator Loss: 0.7349
Epoch 1/1... Batch 400... Discriminator Loss: 1.4641... Generator Loss: 1.2710
Epoch 1/1... Batch 410... Discriminator Loss: 1.1307... Generator Loss: 1.4962
Epoch 1/1... Batch 420... Discriminator Loss: 1.0765... Generator Loss: 1.4242
Epoch 1/1... Batch 430... Discriminator Loss: 0.9149... Generator Loss: 1.8586
Epoch 1/1... Batch 440... Discriminator Loss: 0.8851... Generator Loss: 1.2150
Epoch 1/1... Batch 450... Discriminator Loss: 0.8221... Generator Loss: 1.6409
Epoch 1/1... Batch 460... Discriminator Loss: 2.1652... Generator Loss: 0.1528
Epoch 1/1... Batch 470... Discriminator Loss: 1.4875... Generator Loss: 0.3387
Epoch 1/1... Batch 480... Discriminator Loss: 1.9591... Generator Loss: 0.2241
Epoch 1/1... Batch 490... Discriminator Loss: 1.4752... Generator Loss: 0.3383
Epoch 1/1... Batch 500... Discriminator Loss: 1.1733... Generator Loss: 0.4551
Epoch 1/1... Batch 510... Discriminator Loss: 0.9182... Generator Loss: 0.7336
Epoch 1/1... Batch 520... Discriminator Loss: 1.1855... Generator Loss: 0.8597
Epoch 1/1... Batch 530... Discriminator Loss: 1.1620... Generator Loss: 1.1366
Epoch 1/1... Batch 540... Discriminator Loss: 1.1844... Generator Loss: 0.4767
Epoch 1/1... Batch 550... Discriminator Loss: 1.2699... Generator Loss: 0.4167
Epoch 1/1... Batch 560... Discriminator Loss: 1.3313... Generator Loss: 0.4112
Epoch 1/1... Batch 570... Discriminator Loss: 1.4900... Generator Loss: 0.3212
Epoch 1/1... Batch 580... Discriminator Loss: 1.3026... Generator Loss: 0.4303
Epoch 1/1... Batch 590... Discriminator Loss: 1.0560... Generator Loss: 1.0496
Epoch 1/1... Batch 600... Discriminator Loss: 1.0456... Generator Loss: 1.0875
Epoch 1/1... Batch 610... Discriminator Loss: 1.1542... Generator Loss: 0.5289
Epoch 1/1... Batch 620... Discriminator Loss: 1.0045... Generator Loss: 0.6029
Epoch 1/1... Batch 630... Discriminator Loss: 1.0878... Generator Loss: 0.6503
Epoch 1/1... Batch 640... Discriminator Loss: 1.6124... Generator Loss: 0.2606
Epoch 1/1... Batch 650... Discriminator Loss: 1.2059... Generator Loss: 0.4719
Epoch 1/1... Batch 660... Discriminator Loss: 0.9690... Generator Loss: 0.9076
Epoch 1/1... Batch 670... Discriminator Loss: 0.9216... Generator Loss: 1.1096
Epoch 1/1... Batch 680... Discriminator Loss: 0.9599... Generator Loss: 0.7450
Epoch 1/1... Batch 690... Discriminator Loss: 0.7993... Generator Loss: 1.2080
Epoch 1/1... Batch 700... Discriminator Loss: 1.6557... Generator Loss: 0.2443
Epoch 1/1... Batch 710... Discriminator Loss: 1.4795... Generator Loss: 0.3111
Epoch 1/1... Batch 720... Discriminator Loss: 1.1380... Generator Loss: 0.5205
Epoch 1/1... Batch 730... Discriminator Loss: 1.8203... Generator Loss: 0.2035
Epoch 1/1... Batch 740... Discriminator Loss: 1.1838... Generator Loss: 0.4837
Epoch 1/1... Batch 750... Discriminator Loss: 1.5467... Generator Loss: 0.3370
Epoch 1/1... Batch 760... Discriminator Loss: 1.7666... Generator Loss: 0.2238
Epoch 1/1... Batch 770... Discriminator Loss: 1.6036... Generator Loss: 0.2668
Epoch 1/1... Batch 780... Discriminator Loss: 1.0155... Generator Loss: 1.3842
Epoch 1/1... Batch 790... Discriminator Loss: 0.9270... Generator Loss: 1.2785
Epoch 1/1... Batch 800... Discriminator Loss: 1.6907... Generator Loss: 0.2450
Epoch 1/1... Batch 810... Discriminator Loss: 1.3568... Generator Loss: 0.4221
Epoch 1/1... Batch 820... Discriminator Loss: 1.3753... Generator Loss: 0.3486
Epoch 1/1... Batch 830... Discriminator Loss: 1.4619... Generator Loss: 0.3146
Epoch 1/1... Batch 840... Discriminator Loss: 1.4711... Generator Loss: 0.3119
Epoch 1/1... Batch 850... Discriminator Loss: 1.1291... Generator Loss: 1.3837
Epoch 1/1... Batch 860... Discriminator Loss: 1.2214... Generator Loss: 1.7129
Epoch 1/1... Batch 870... Discriminator Loss: 1.0630... Generator Loss: 1.3398
Epoch 1/1... Batch 880... Discriminator Loss: 0.9367... Generator Loss: 0.9300
Epoch 1/1... Batch 890... Discriminator Loss: 1.2151... Generator Loss: 1.5026
Epoch 1/1... Batch 900... Discriminator Loss: 1.0411... Generator Loss: 0.9121
Epoch 1/1... Batch 910... Discriminator Loss: 1.7076... Generator Loss: 0.2274
Epoch 1/1... Batch 920... Discriminator Loss: 1.8673... Generator Loss: 0.2063
Epoch 1/1... Batch 930... Discriminator Loss: 1.7759... Generator Loss: 0.2182
Epoch 1/1... Batch 940... Discriminator Loss: 1.1482... Generator Loss: 1.8216
Epoch 1/1... Batch 950... Discriminator Loss: 0.7551... Generator Loss: 1.0100
Epoch 1/1... Batch 960... Discriminator Loss: 1.0517... Generator Loss: 0.6787
Epoch 1/1... Batch 970... Discriminator Loss: 0.9469... Generator Loss: 0.9568
Epoch 1/1... Batch 980... Discriminator Loss: 1.1510... Generator Loss: 0.4851
Epoch 1/1... Batch 990... Discriminator Loss: 1.2298... Generator Loss: 0.4536
Epoch 1/1... Batch 1000... Discriminator Loss: 1.6202... Generator Loss: 0.2624
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Epoch 1/1... Batch 1080... Discriminator Loss: 1.2209... Generator Loss: 0.4286
Epoch 1/1... Batch 1090... Discriminator Loss: 1.5014... Generator Loss: 0.3050
Epoch 1/1... Batch 1100... Discriminator Loss: 2.1651... Generator Loss: 0.1426
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Epoch 1/1... Batch 1200... Discriminator Loss: 1.2026... Generator Loss: 2.0623
Epoch 1/1... Batch 1210... Discriminator Loss: 1.1716... Generator Loss: 1.2590
Epoch 1/1... Batch 1220... Discriminator Loss: 1.4063... Generator Loss: 0.3273
Epoch 1/1... Batch 1230... Discriminator Loss: 2.2732... Generator Loss: 0.1275
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Epoch 1/1... Batch 1310... Discriminator Loss: 1.6151... Generator Loss: 0.2651
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Epoch 1/1... Batch 1390... Discriminator Loss: 1.0685... Generator Loss: 2.3921
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Epoch 1/1... Batch 1420... Discriminator Loss: 0.9425... Generator Loss: 1.1442
Epoch 1/1... Batch 1430... Discriminator Loss: 0.8560... Generator Loss: 0.9977
Epoch 1/1... Batch 1440... Discriminator Loss: 0.6141... Generator Loss: 1.7634
Epoch 1/1... Batch 1450... Discriminator Loss: 1.7383... Generator Loss: 0.2475
Epoch 1/1... Batch 1460... Discriminator Loss: 1.8628... Generator Loss: 0.1968
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Epoch 1/1... Batch 1480... Discriminator Loss: 1.9746... Generator Loss: 0.1787
Epoch 1/1... Batch 1490... Discriminator Loss: 1.7789... Generator Loss: 0.2278
Epoch 1/1... Batch 1500... Discriminator Loss: 1.5204... Generator Loss: 0.2968
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Epoch 1/1... Batch 1740... Discriminator Loss: 1.1991... Generator Loss: 0.5238
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Epoch 1/1... Batch 2180... Discriminator Loss: 1.7672... Generator Loss: 4.4470
Epoch 1/1... Batch 2190... Discriminator Loss: 0.3209... Generator Loss: 2.1822
Epoch 1/1... Batch 2200... Discriminator Loss: 2.5627... Generator Loss: 4.3805
Epoch 1/1... Batch 2210... Discriminator Loss: 1.2954... Generator Loss: 2.2839
Epoch 1/1... Batch 2220... Discriminator Loss: 0.2258... Generator Loss: 3.3597
Epoch 1/1... Batch 2230... Discriminator Loss: 2.3383... Generator Loss: 3.9308
Epoch 1/1... Batch 2240... Discriminator Loss: 0.4808... Generator Loss: 1.1186
Epoch 1/1... Batch 2250... Discriminator Loss: 1.9266... Generator Loss: 0.2005
Epoch 1/1... Batch 2260... Discriminator Loss: 0.7460... Generator Loss: 3.3914
Epoch 1/1... Batch 2270... Discriminator Loss: 0.2365... Generator Loss: 6.0761
Epoch 1/1... Batch 2280... Discriminator Loss: 0.8160... Generator Loss: 0.6570
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Epoch 1/1... Batch 2300... Discriminator Loss: 1.9555... Generator Loss: 0.1890
Epoch 1/1... Batch 2310... Discriminator Loss: 0.4119... Generator Loss: 1.3014
Epoch 1/1... Batch 2320... Discriminator Loss: 0.6850... Generator Loss: 0.8345
Epoch 1/1... Batch 2330... Discriminator Loss: 1.8451... Generator Loss: 3.4233
Epoch 1/1... Batch 2340... Discriminator Loss: 1.2118... Generator Loss: 0.4887
Epoch 1/1... Batch 2350... Discriminator Loss: 0.9953... Generator Loss: 1.0074
Epoch 1/1... Batch 2360... Discriminator Loss: 0.3582... Generator Loss: 3.0391
Epoch 1/1... Batch 2370... Discriminator Loss: 0.3550... Generator Loss: 1.6200
Epoch 1/1... Batch 2380... Discriminator Loss: 1.4041... Generator Loss: 4.9669
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Epoch 1/1... Batch 2410... Discriminator Loss: 0.5459... Generator Loss: 4.1484
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Epoch 1/1... Batch 2430... Discriminator Loss: 1.8362... Generator Loss: 0.2141
Epoch 1/1... Batch 2440... Discriminator Loss: 1.8511... Generator Loss: 0.1999
Epoch 1/1... Batch 2450... Discriminator Loss: 1.0404... Generator Loss: 4.0235
Epoch 1/1... Batch 2460... Discriminator Loss: 0.2056... Generator Loss: 3.7305
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Epoch 1/1... Batch 2490... Discriminator Loss: 1.4561... Generator Loss: 0.3040
Epoch 1/1... Batch 2500... Discriminator Loss: 0.9511... Generator Loss: 0.6320
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Epoch 1/1... Batch 2520... Discriminator Loss: 0.4581... Generator Loss: 1.2931
Epoch 1/1... Batch 2530... Discriminator Loss: 0.5911... Generator Loss: 1.0350
Epoch 1/1... Batch 2540... Discriminator Loss: 1.1111... Generator Loss: 0.4876
Epoch 1/1... Batch 2550... Discriminator Loss: 0.3554... Generator Loss: 3.5627
Epoch 1/1... Batch 2560... Discriminator Loss: 0.8106... Generator Loss: 0.7469
Epoch 1/1... Batch 2570... Discriminator Loss: 0.0905... Generator Loss: 3.5384
Epoch 1/1... Batch 2580... Discriminator Loss: 3.1616... Generator Loss: 0.0518
Epoch 1/1... Batch 2590... Discriminator Loss: 1.5310... Generator Loss: 0.3072
Epoch 1/1... Batch 2600... Discriminator Loss: 0.1559... Generator Loss: 2.5248
Epoch 1/1... Batch 2610... Discriminator Loss: 1.6508... Generator Loss: 0.2663
Epoch 1/1... Batch 2620... Discriminator Loss: 0.9422... Generator Loss: 0.6080
Epoch 1/1... Batch 2630... Discriminator Loss: 0.6479... Generator Loss: 3.8964
Epoch 1/1... Batch 2640... Discriminator Loss: 0.6766... Generator Loss: 0.8800
Epoch 1/1... Batch 2650... Discriminator Loss: 1.2452... Generator Loss: 0.3930
Epoch 1/1... Batch 2660... Discriminator Loss: 0.6758... Generator Loss: 0.8496
Epoch 1/1... Batch 2670... Discriminator Loss: 1.3959... Generator Loss: 4.1154
Epoch 1/1... Batch 2680... Discriminator Loss: 1.8855... Generator Loss: 0.1852
Epoch 1/1... Batch 2690... Discriminator Loss: 0.3679... Generator Loss: 3.9032
Epoch 1/1... Batch 2700... Discriminator Loss: 2.0455... Generator Loss: 0.1669
Epoch 1/1... Batch 2710... Discriminator Loss: 0.6782... Generator Loss: 3.0153
Epoch 1/1... Batch 2720... Discriminator Loss: 0.1920... Generator Loss: 2.4760
Epoch 1/1... Batch 2730... Discriminator Loss: 0.8009... Generator Loss: 0.9737
Epoch 1/1... Batch 2740... Discriminator Loss: 0.5570... Generator Loss: 1.0386
Epoch 1/1... Batch 2750... Discriminator Loss: 2.1337... Generator Loss: 0.1559
Epoch 1/1... Batch 2760... Discriminator Loss: 0.2592... Generator Loss: 2.9341
Epoch 1/1... Batch 2770... Discriminator Loss: 0.8544... Generator Loss: 2.2305
Epoch 1/1... Batch 2780... Discriminator Loss: 0.2720... Generator Loss: 2.7982
Epoch 1/1... Batch 2790... Discriminator Loss: 1.0805... Generator Loss: 3.2707
Epoch 1/1... Batch 2800... Discriminator Loss: 0.1674... Generator Loss: 3.8017
Epoch 1/1... Batch 2810... Discriminator Loss: 1.2595... Generator Loss: 0.4047
Epoch 1/1... Batch 2820... Discriminator Loss: 0.2623... Generator Loss: 1.6762
Epoch 1/1... Batch 2830... Discriminator Loss: 0.3203... Generator Loss: 1.7981
Epoch 1/1... Batch 2840... Discriminator Loss: 2.5399... Generator Loss: 0.1035
Epoch 1/1... Batch 2850... Discriminator Loss: 0.0665... Generator Loss: 3.8219
Epoch 1/1... Batch 2860... Discriminator Loss: 1.7648... Generator Loss: 0.2398
Epoch 1/1... Batch 2870... Discriminator Loss: 1.2779... Generator Loss: 5.8124
Epoch 1/1... Batch 2880... Discriminator Loss: 0.1855... Generator Loss: 3.6010
Epoch 1/1... Batch 2890... Discriminator Loss: 0.1271... Generator Loss: 3.6688
Epoch 1/1... Batch 2900... Discriminator Loss: 0.7047... Generator Loss: 4.0883
Epoch 1/1... Batch 2910... Discriminator Loss: 0.7694... Generator Loss: 6.0537
Epoch 1/1... Batch 2920... Discriminator Loss: 0.8062... Generator Loss: 0.7279
Epoch 1/1... Batch 2930... Discriminator Loss: 1.6171... Generator Loss: 0.2697
Epoch 1/1... Batch 2940... Discriminator Loss: 0.5354... Generator Loss: 1.6231
Epoch 1/1... Batch 2950... Discriminator Loss: 0.7309... Generator Loss: 3.4065
Epoch 1/1... Batch 2960... Discriminator Loss: 0.4454... Generator Loss: 3.6823
Epoch 1/1... Batch 2970... Discriminator Loss: 1.5580... Generator Loss: 6.1587
Epoch 1/1... Batch 2980... Discriminator Loss: 0.2287... Generator Loss: 1.7309
Epoch 1/1... Batch 2990... Discriminator Loss: 1.3700... Generator Loss: 0.3776
Epoch 1/1... Batch 3000... Discriminator Loss: 0.3211... Generator Loss: 1.4976
Epoch 1/1... Batch 3010... Discriminator Loss: 0.3834... Generator Loss: 3.1108
Epoch 1/1... Batch 3020... Discriminator Loss: 0.6800... Generator Loss: 0.8243
Epoch 1/1... Batch 3030... Discriminator Loss: 3.3770... Generator Loss: 0.0452
Epoch 1/1... Batch 3040... Discriminator Loss: 0.6934... Generator Loss: 3.2977
Epoch 1/1... Batch 3050... Discriminator Loss: 0.7699... Generator Loss: 0.8188
Epoch 1/1... Batch 3060... Discriminator Loss: 0.7901... Generator Loss: 0.7561
Epoch 1/1... Batch 3070... Discriminator Loss: 2.1049... Generator Loss: 0.1535
Epoch 1/1... Batch 3080... Discriminator Loss: 2.2569... Generator Loss: 0.1418
Epoch 1/1... Batch 3090... Discriminator Loss: 0.8395... Generator Loss: 0.7256
Epoch 1/1... Batch 3100... Discriminator Loss: 0.8386... Generator Loss: 0.7200
Epoch 1/1... Batch 3110... Discriminator Loss: 2.0984... Generator Loss: 0.1795
Epoch 1/1... Batch 3120... Discriminator Loss: 1.6459... Generator Loss: 0.2589
Epoch 1/1... Batch 3130... Discriminator Loss: 0.1895... Generator Loss: 2.5319
Epoch 1/1... Batch 3140... Discriminator Loss: 1.0195... Generator Loss: 0.5596
Epoch 1/1... Batch 3150... Discriminator Loss: 0.8025... Generator Loss: 0.8091
Epoch 1/1... Batch 3160... Discriminator Loss: 1.0394... Generator Loss: 0.5938

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.